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[ai-geostats] Regression vs. Kriging vs. Simulation vs. IDW

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  • Isobel Clark
    Agrred, IDW is a good rough way to visualise your data before embarking on more objective (?) approaches. If your data is pretty regularly spread out, small
    Message 1 of 16 , Jan 4, 2005
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      Agrred, IDW is a good rough way to visualise your data
      before embarking on more 'objective'(?) approaches.

      If your data is pretty regularly spread out, small
      nugget effect and you use the semi-variogram to choose
      the search radii, there is little difference between
      an IDW-squared map and kriging.

      Isobel
    • Digby Millikan
      Seumas, I was probably a bit misleading to say regression is not an estimation technique. The word regression meaning to revert back to the original, or find
      Message 2 of 16 , Jan 5, 2005
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        Seumas,

        I was probably a bit misleading to say regression
        is not an estimation technique. The word regression
        meaning to revert back to the original, or find the
        underlying real equation for a set of data. "Kriging"
        is a form of what is called "generalised linear regression"
        which is one of the most advanced forms of regression.
        The simpler forms of regression can be used to fit
        parametrics equations to data, such as linear regression
        to fit an equation of a line to a set of data points,
        or non-linear regression to fit a polynomial surface
        to a scattered set of say topography data points.
        Not really estimation, but equation fitting. I use non-linear
        regression to fit equations to drillhole survey points
        to plot their curves. In it's more advanced form when
        you wish to fit equations to say a set of two dimensional
        data points, or three dimensional orebody samples,
        this is called trend surface fitting. Unfortunately normally
        the equations developed from trend surface fitting
        become massively too complex to handle to be practical,
        and hence estimation is opted for.

        Digby
      • Digby Millikan
        For ore resource modelling I ve used IDW on a highly skewed lognormally distributed deposit, where no variograms could be produced. With lognormally
        Message 3 of 16 , Jan 5, 2005
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          For ore resource modelling I've used IDW on a highly skewed lognormally
          distributed deposit, where no variograms could be produced. With lognormally
          distributed data often found in ore resources, having a good variogram is
          important, to avoid large errors in kriging hence it may be preferential to
          use
          IDW and a topcut. However if your data is not so highly skewed even
          approximating
          a variogram can provide superior results. I used to model topography
          surfaces
          and Kriging with a 'guessed' variogram produced good results compared to
          IDW which produced highly spiked and erroneous results.

          Digby
          www.users.on.net/~digbym
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